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The Better Bunion System is intended to be used as a surgical instrument to assist in pre-operative planning and/or in guiding the marking of bone and/or guide surgical instruments in non-acute, non-joint replacing osteotomies in the foot and ankle for adult and pediatric patients 12 years of age and older. Better Bunion cutting guides are intended for single use only.

The RedPoint Medical Better Bunion system includes single use, patient specific bone resection guides designed from DICOM files from a patients' CT scans and a surgeon's prescription. The Better Bunion system includes single use and reusable instruments to facilitate surgery. The Better Bunion patient specific bone resection guides assist the surgeon in cutting bone in the foot and ankle according to the pre-surgical plan. The quides are individually manufactured for each patient using a validated design and manufacturing process with strict procedures for transfer and conversion of patient images from DICOM files to digital models (STL files), and in turn to patient specific bone resection guides that are additively manufactured with titanium alloy conforming to ASTM F3001. The bone cutting guides are single use devices and provided clean, not sterile to the end user.

Here's a breakdown of the acceptance criteria and study information for the RedPoint Medical Better Bunion System, based on the provided FDA 510(k) summary:

Acceptance Criteria and Reported Device Performance

As an AI, I need to point out that the provided text is an FDA 510(k) summary, which often provides a high-level overview of performance data rather than a detailed study protocol. Therefore, some of the requested information (like specific sample sizes for test sets, expert qualifications, or details about comparative effectiveness studies) is not explicitly stated in this document. I will extract what is available and note when information is not present.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Sample Size: Not explicitly stated for the "simulated surgical studies." The text mentions "all guides" when referring to the IM angle performance, but doesn't quantify the number of guides or cases.
• Data Provenance: Not explicitly stated. The studies are described as "simulated surgeries," implying a controlled, prospective setup, but the specific origin (e.g., country) is not mentioned.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• This information is not provided in the summary. The "simulated surgical studies" suggest an objective measurement against a plan, but the role of experts in establishing ground truth for individual cases is not detailed.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

• This information is not provided in the summary.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• A MRMC comparative effectiveness study is not mentioned. The performance data focuses on the device's accuracy in achieving a planned outcome in simulated surgeries, not on how it assists or improves human reader performance or diagnostic accuracy. The device is a surgical instrument/guide, not a diagnostic AI.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done

• The reported performance (simulated surgeries for correction of hallux valgus deformities) appears to be analogous to a "standalone" performance for the guide itself. The guide's accuracy is measured against the pre-surgical plan. While a surgeon would use the guide, the measurement of the guide's precision in achieving the planned angles in simulation reflects its inherent accuracy as designed, before human variability in actual surgery is introduced.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• The ground truth for the simulated surgery results (IM angle deviation) was the pre-surgical plan. The device's performance was measured by how closely it enabled the achievement of this predefined plan (e.g., final average IM angle of ±2º relative to plan, average deviation of 0.91º relative to plan).
• For the "fit and usability" verification, the ground truth was likely observational assessment and functional testing against engineering specifications and expected surgical workflow.
• For biocompatibility and sterilization, the ground truth was adherence to international standards (ISO 10993-1, AAMI/ISO 14927) and achieving specified performance targets (e.g., SAL of 10^-6^).

8. The sample size for the training set

• The device is a patient-specific surgical guide, designed from individual patient CT scans. It is not an AI algorithm in the sense of a machine learning model that requires a "training set" of patient data in the conventional way.
• The text describes a "validated design and manufacturing process with strict procedures for transfer and conversion of patient images from DICOM files to digital models (STL files), and in turn to patient specific bone resection guides." This process itself would have been developed and validated, but there isn't a "training set" of patient images in the typical AI sense for the device itself. The "training" would be more akin to software and manufacturing process validation.

9. How the ground truth for the training set was established

• As noted above, there isn't a "training set" in the conventional AI sense for this type of device. The ground truth for the design and manufacturing process validation (which ensures accurate translation from DICOM to guide) would involve:
  • Metrological assessments: Comparing digital models (STL files) derived from DICOM data to known anatomical structures or highly accurate scans.
  • Prototyping and testing: Manufacturing guides and verifying their fit and accuracy on physical models or cadaveric specimens, ensuring they align with the pre-surgical plans generated.
  • Software validation: Ensuring the conversion software accurately transforms DICOM data into digital bone models and then into guide designs.

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